The identity design of artificial agents carries growing ethical, psychological, and cultural weight, as ubiquitous language models and diverse robotic forms are blended into everyday use. However, structured approaches to designing coherent and interpretable artificial identities remain limited. To address urgent challenges in artificial identity design, including harmful stereotypes and deceptive practices, we introduce the Identity Design (ID) Framework and an accompanying research agenda. Drawing on emerging work on artificial identity in human-robot interaction and taking an interdisciplinary perspective, we propose twelve design principles across three levels: individual (recognisability, behavioural consistency, identity continuity, memory, persistent goals), group (membership signalling, social alignment, role clarity), and societal (benevolence, artificiality, social justice, transparency). The research agenda outlines open questions around the operationalisation and measurement of identity, social dynamics, and ethical considerations for identity design. Together, they lay the groundwork for future research and responsible practice in robotic, virtual, and multi-embodied agents.

Human-Robot Interaction (HRI) researchers use diverse methodologies for empirical, hypothesis-driven research, including laboratory experiments, longitudinal field deployments, and online experiments. Within these, field deployments are typically seen as suffering from lessened ecological control, and online experiments are seen as suffering from lessened ecological validity. Yet HRI researchers have largely ignored other threats to validity that uniquely emerge at the center of this spectrum.

For human operators to effectively task teams of robots, it is critical that they maintain situational awareness about the status of those robots. However, maintaining this situational awareness becomes particularly difficult when there are dynamic changes not only in the members of the robot team, but also in the capabilities of those robots. Prior work has shown that situational awareness can be supported through interfaces that effectively visualize task-relevant information. As such, in this work, we introduce a Capability-Level System for Tracking Robots (CLSTR), a new visualization for supporting operators to maintain an appropriate level of situational awareness over the capabilities of a dynamic robot team. In evaluating CLSTR through an online human-subject study ($\mathbf{n} \boldsymbol{=} \mathbf{1 2 3}$), we found that a combination of different visual elements within an interface like the use of icons to summarize robot capabilities and animations to indicate team changes can help operators maintain awareness over robot teams.

Wizard-of-Oz (WoZ) is one of the most widely used experimental methodologies across the field of Human-Robot Interaction (HRI), making WoZ teleoperation interfaces a critical tool for HRI research. Yet current WoZ teleoperation interfaces are overwhelmingly tailored towards a narrow set of HRI interaction paradigms. In this work, we conducted a set of interviews with HRI researchers to better understand the diversity of teleoperation needs across the HRI community. Our analysis highlighted (1) human challenges, with respect to wizards’ expertise, the need for quick responses, and research participants’ unpredictability; (2) robot challenges, with respect to robot malfunctions, delays, and robot-driven complexity, and (3) interaction challenges, with respect to researchers’ varying control requirements and the need for precise experimental control. Moreover, our results revealed unexpected parallels between the experiences of HRI researchers and real-world teleoperators, which open up fundamentally new possibilities for future work in robot control interfaces and encourage radically different perspectives on what types of interfaces are even needed to best facilitate WoZ experimentation. Leveraging these insights, we recommend that WoZ interfaces (1) be designed with extensibility and customization in mind, (2) ease interaction management by accounting for unpredictability and multi-robot interactions, and (3) consider WoZ teleoperators beyond the context of experimentation.

To critically analyze and adapt to the risks and benefits of social robotics, future user communities will require technology and AI literacy: the ability to use new robotic technologies, understand their strengths and limitations, and critically evaluate the implications of their use. Research shows that collaborative, creative, and informal learning experiences can support AI literacy among non-technologists. Therefore, we designed Degrees of Freedom, a multiplayer interactive storytelling game that supports technology literacy about social robots. Degrees of Freedom supports technology literacy competencies by encouraging players to explore how values are encoded in robot designs, compelling players to consider the risks and limitations of robots, and encouraging them to make connections to their own lives and values. We present both the design of Degrees of Freedom and the results of game playtesting. Our results show that the narrative, collaborative nature of the game supported players in critical thinking about the role robots can or should have in their communities.

Human interactions with robot groups are more complex than interactions with individual robots. This is especially true for groups of robots that do not have humanlike 1-1 associations between bodies and identities, such as when multiple robots share a single identity. This is further complicated by the lack of direct observability of the relationship between body and identity, which may be inferred by users on the basis of various robot group identity performance strategies. Previous research on Deconstructed Trustee Theory has argued that this complexity is critical, as different perceived bodyidentity configurations may lead users to build and develop trust in distinct ways. In this paper, we thus investigate (n=94) the ways that different robot group identity performance strategies might influence the distribution of trust amongst robot group members, as well as the impact of these strategies on perceptions of robot group entitativity.

Research has shown that the relationship between robot mind, body, and identity is flexible and can be performed in a variety of ways. Our research explores how identity performance strategies used among robot groups may be presented through group identity observables (design cues), and how those strategies impact human-robot interactions. Specifically, we ask how group identity observables lead observers to develop different mental models of robot groups, and different perceptions of trust and group dynamics constructs.

Research has shown how the connections between robots' minds, bodies, and identities can be configured and performed in a variety of ways. In this work, we consider group identity observables: the set of design cues that robot groups use to perform different identity configurations. We explore how group identity observables lead observers to develop different mental models of robot groups. Specifically, we make four key contributions: (1) we define, conceptualize, and taxonomize group identity observables; (2) we use Grounded Theory-informed analysis of qualitative data to produce a taxonomy of users' mental models invoked by variation in those observables; (3) we empirically demonstrate (n=166) how variations in observables lead to different mental models; and (4) we further demonstrate how variations in those observables, and the mental models they evoke, influence key group dynamics constructs like entitativity.

Children are stakeholders of robotic technologies who deserve to have their voices heard in the design process just as much as adult stakeholders. This is especially true for robotic technologies explicitly designed for child-robot interaction, in areas like education, healthcare, and therapy. Researchers face the challenge of cultivating children's critical awareness on the design of robots and accompanying ethical concerns, as the types of exercises typically used to engage with adult stakeholders can be ineffective with children. This requires developmentally appropriate methods for understanding children's perspectives that also address the imbalanced power dynamics between children and adults-such that children feel comfortable sharing their ideas. In this work, we demonstrate that participatory design research techniques already accepted in the Human Robot Interaction (HRI) community can fulfill this purpose. Specifically, through the design and analysis of two co-design workshops with children of different ages at a school in Denver, Colorado, we demonstrate that co-design workshops can be used to effectively understand how children make sense of robotic technologies and to facilitate children's critical reflection on the ethical dilemmas surrounding their own relationships with robots.

Online CS education shows many gender-inclusivity problems in practice and through tools, and little has been done to address this. In CS classrooms, pair programming has been shown to significantly help women and men understand and appreciate programming concepts, and to help close gender gaps. Unfortunately, pair programming is not well supported in online CS education, especially when one of the pair members is a woman. Our overall objective is to address this gap by investigating the following question: How can we bring the educational benefits of pair programming to online CS students in gender-equitable ways? In this paper, we empirically investigate whether and how technology-mediated remote pair programming hinders online students of same- and mixed-gender pairs. Based on our results, we propose refining personas and the cognitive walkthrough to include leadership styles and preferences for pair-programming roles. We further recommend features for online CS educational tools to promote gender-inclusiveness.